High-throughput pipelined and scalable architecture for a K-Best MIMO detector

ABSTRACT

A high throughput and scalable MIMO detector can use a K-Best detection algorithm to find K combinations of transmit symbols that are likely to be the symbols that were actually transmitted. The K-best MIMO detector can include a plurality of stages, where each stage may correspond to a transmit antenna, and each stage can find K best symbol combinations based on information from a previous stage. To find the new K best symbol combinations, at each stage, a plurality of metrics for potential combinations are computed and sorted by magnitude. The MIMO detector preferably uses a high throughput, merge sorting algorithm to sort the metrics.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.11/934,351, filed Nov. 2, 2007 (currently pending), which claims thebenefit under 35 U.S.C. §119(e) of U.S. Provisional Application No.60/864,653, filed Nov. 7, 2006, which are hereby incorporated herein byreference in their entireties.

BACKGROUND OF THE INVENTION

The disclosed technology relates generally to signal detection, and moreparticular to detecting signals received in a multiple-inputmultiple-output (MIMO) system using a K-Best MIMO detector.

With the continuing demand for higher-speed digital communicationssystems and higher-density digital storage systems, various techniqueshave been applied to increase the capacity of these systems. However,even with high-capacity communications and storage media, theirrespective bandwidths and densities are still limited. Therefore, MIMOsystems are often used to fully exploit the capabilities of thesesystems. In particular, increasing the dimensions of these systemsenables higher throughput and reliability, as more information can beconveyed without increasing the bandwidth of the system.

However, the efficiency gained by MIMO systems comes at the expense of acomplex receiver design. In particular, a MIMO receiver may obtain aplurality of signals from its multiple receiver inputs, where eachsignal includes information from each of the multiple transmitteroutputs. From all of this jumbled information, a MIMO detector canattempt to recover the independent information transmitted from each ofthe various transmitter outputs. Thus, to maintain high reliability,this information recovery process can be highly complex and low inthroughput. Therefore, it would be desirable to provide low complexity,high throughput, and scalable architectures for detecting signalsreceived from a MIMO system.

SUMMARY OF THE INVENTION

Accordingly, apparatus and methods are provided for a scalable K-BestMIMO detector with high throughput and low complexity.

Information in a digital communications or stage system can betransmitted from a source to a sink. To accurately transfer thisinformation, a transmitter for transmitting the information may be usedlocal to the source, and a receiver for accurately detecting theinformation can be used local to the sink. In some embodiments, such asin a wireless transmission system, the transmitter may include aplurality of transmit antennas and the receiver may include a pluralityof receive antennas. Thus, the communications or storage system can bereferred to as a multiple-input multiple-output (MIMO) system.

In accordance with an embodiment of the present invention, digitalinformation from a transmitter can be estimated by a MIMO detectorincluded in a MIMO receiver. For each transmitted signal from a transmitantenna, a plurality of metrics can be computed. Each metric may beassociated with a possible symbol transmitted by that transmit antenna,as well as possible symbols transmitted by other transmit antennas, andpreferably indicates the likelihood of these associated symbols. Thecomputed metrics may be sorted into an order based on magnitude using amerge sort algorithm. A predetermined number, or K, of the sortedmetrics can be selected by, for example, choosing the metrics withsmallest magnitude. Thus, the selected metrics preferably correspond tocombinations of symbols that are likely to have been transmitted fromthe transmit antennas. An estimate of transmitted digital informationcan be calculated based on the predetermined number of sorted metrics.The estimates may be soft information in the form of, for example, loglikelihood ratios.

In some embodiments, the plurality of metrics can be calculated in aplurality of stages, where each stage is associated with a particularreceived signal. If the associated channel response matrix is uppertriangular, or converted to be upper triangular, each stage preferablyis also associated with a particular transmitted signal. The pluralityof metrics can be sorted by sorting only those metrics within a samestage. From the sorted metrics for a particular stage, a predeterminednumber of intermediate metrics can be selected. The intermediate metricsmay be associated with a subset of branches in a tree diagram (e.g.,tree diagram 500 of FIG. 5), and may be selected for having the smallestmagnitude. These intermediate metrics can be used by a next stage tocalculate its plurality of metrics. Thus, the metrics computed in thenext stage can be based on likely values of transmitted signalsassociated with previous stages. The predetermined number of selectedmetrics from a final stage, which preferably includes information aboutevery transmit signal, can be used to estimate the digital information.

In accordance with another embodiment of the present invention, likelycombinations of transmitted symbols in a MIMO system can be discovered.The likely symbols for each transmitted symbol can be found sequentially(e.g., the symbol transmitted from the final transmit antenna to thesymbol transmitted from the first transmit antenna). A set of likelysymbols for a particular transmitter output in the sequence can be foundby first computing multiple sets of metrics, where each set of metricspreferably corresponds to a combination of symbols for other transmitteroutputs. Also, each metric in a set of metrics preferably is associatedwith a possible symbol for the particular transmit output. Each set ofmetrics can be individually sorted using a merge sorting algorithm. Theresults of each set of sorted metrics can then be combined and sortedusing a merge sorting algorithm. A predetermined number of these sortedmetrics can be selected by, for example, selecting metrics with smallestvalue. These predetermined number of selected metrics can then be usedto find the set of likely symbols for the next transmitter output in thesequence.

BRIEF DESCRIPTION OF THE FIGURES

The above and other objects and advantages of the invention will beapparent upon consideration of the following detailed description, takenin conjunction with the accompanying drawings, in which like referencecharacters refer to like parts throughout, and in which:

FIG. 1 shows a simplified illustrative multiple-input multiple-output(MIMO) digital communications or storage system;

FIG. 2 shows an illustrative wireless communication system in accordancewith an embodiment of FIG. 1;

FIG. 3 shows an illustrative MIMO transmitter and receiver;

FIGS. 4A-4B show illustrative signal constellation sets that can be usedby a modulator to transmit symbols;

FIG. 5 shows an illustrative tree diagram that represents transmittedinformation in a MIMO system;

FIG. 6 shows an illustrative K-Best MIMO detector;

FIG. 7 shows a simplified block diagram of a stage of the K-Best MIMOdetector of FIG. 6;

FIG. 8 illustrates the operation of the detector stage of FIG. 7;

FIG. 9A shows a more detailed, yet still simplified, block diagram of amerge sort unit in a stage of a K-Best MIMO detector;

FIG. 9B shows an embodiment of the merge sort unit of FIG. 9A;

FIG. 10 shows a simplified block diagram of a stage of a K-Best MIMOdetector that includes a unit for reducing the number of constellationpoints considered in the stage;

FIGS. 11A-11B show illustrative regions in signal constellation sets;

FIG. 12 shows an embodiment of the detector stage of FIG. 10;

FIG. 13 shows an illustrative flow diagram for decoding received signalsfrom a MIMO system;

FIG. 14 shows an illustrative flow diagram for finding the K bestsymbols in a signal constellation set;

FIG. 15 shows an illustrative flow diagram for sorting metrics;

FIG. 16 shows an illustrative flow diagram for reducing the number ofsignal points considered by a stage of a detector;

FIG. 17A is a block diagram of an exemplary hard disk drive that canemploy the disclosed technology;

FIG. 17B is a block diagram of an exemplary digital versatile disc thatcan employ the disclosed technology;

FIG. 17C is a block diagram of an exemplary high definition televisionthat can employ the disclosed technology;

FIG. 17D is a block diagram of an exemplary vehicle that can employ thedisclosed technology;

FIG. 17E is a block diagram of an exemplary cell phone that can employthe disclosed technology;

FIG. 17F is a block diagram of an exemplary set top box that can employthe disclosed technology; and

FIG. 17G is a block diagram of an exemplary media player that can employthe disclosed technology.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 shows illustrative system 100 of a basic multiple-inputmultiple-output (MIMO) digital communications or storage system inaccordance with an embodiment of the present invention. System 100 caninclude source 102, transmitter 106, receiver 110 and sink 112. System100 can convey information, including digital information 104, fromsource 102 to sink 112 using transmitter 106 and receiver 110. Digitalinformation 104 can represent any type of information to be conveyed tosink 112, such as a sampled/quantized version of an analog signal orbinary information. Digital information 104 may be provided or generatedby source 102. Source 102 can be any suitable type of digitalinformation source including, but not limited to, a source encoder, amagnetic storage device (e.g., a hard disk), an electrical storagedevice (e.g., FLASH memory or RAM), or an optical storage device (e.g.,a CD-ROM).

The information transferred from source 102 to sink 112 may travelthrough some transmission or playback medium. This medium is illustratedas channel 108 in FIG. 1. Channel 108 can represent any transmission orstorage medium or media, and can alter information being transmittedthrough it. Thus, transmitter 106 and receiver 110 can be used toaccurately transmit and receive information through channel 108. Inparticular, transmitter 106 preferably conveys digital information 102in a manner appropriate for transmission through channel 108, andreceiver 110 preferably interprets received information based on theproperties of channel 108. Transmitter 106 can have a plurality ofoutputs for transmitting information and receiver 110 can have aplurality of inputs for receiving information. For simplicity, thevariable, M, will hereinafter represent the number of transmitteroutputs, and the variable, P, will hereinafter represent the number ofreceiver inputs. Thus, transmitter 106 can have outputs TX_1, . . . ,TX_M, and receiver 110 can have inputs RX_1, . . . , RX_P. Also, channel108 may be referred to as a MIMO channel with M inputs and P outputs, orsimply an M×P MIMO channel.

It should be understood that the enumeration of transmitter outputs(e.g., TX_1, . . . , TX_M) and receiver inputs (e.g., RX_1, . . . ,RX_P) are for convenience only, and are not meant to suggest aparticular ordering of the outputs. For example, it should not beassumed that the numbering is based on a spatial orientation of thevarious inputs/outputs, or that the enumeration suggests a relativepriority of the various inputs/outputs.

Referring now to FIG. 2, illustrative wireless communication system 200is shown in accordance with an embodiment of the present invention.Wireless system 200 may include any of the features and functionalitiesof the components in system 100 of FIG. 1. In this embodiment, wirelesstransmitter 202 may be a transmitter for broadcasting informationwirelessly, such as a commercial gateway modem. Wireless transmitter 202preferably transmits information from a source using M wireless antennas204. Wireless receiver 206 can receive information wirelessly, and maybe, for example, a commercial wireless computer adapter. Wirelessreceiver 206 can receive information transmitted from wirelesstransmitter 202 using P receive antennas 206, and may receive thisinformation from channel 208. Channel 208 may include the space betweentransmit antennas 204 and receive antennas 206, and may include anystructures within the space that may obstruct and attenuate thetransmitted signals. For example, channel 208 can alter the transmittedsignals due to at least multipath fades and shadowing effects.

Referring now to FIG. 3, illustrative block diagram 300 is shown oftransmitter 302 and receiver 304 in accordance with an embodiment of thepresent invention. Transmitter 302 and receiver 304 may have any of thefeatures and functionalities of transmitter 106 and receiver 110 in FIG.1, respectively. In some embodiments, transmitter 302 and receiver 304may be a wireless transmitter and a wireless receiver, such as thoseshown in FIG. 2. Transmitter 302 can include encoder 306, spatialmultiplexer 308, and modulators 310. Encoder 306 may encode digitalinformation (e.g., digital information 104 of FIG. 1) based on anysuitable error correcting or error detecting code. In some embodiments,encoder 306 may be a convolutional encoder, a Reed-Solomon encoder, or aCRC encoder or advanced encoding scheme such as convolutional turbocodes or low density parity check codes. Using these or another suitableerror control code, encoder 306 may output a digital sequence with addedredundancy that can improve the reliability of the overall system.

With continuing reference to FIG. 3, spatial multiplexer 308 preferablymultiplexes the digital sequence provided by encoder 306 into M separatesequences. For example, multiplexer 308 may provide each successive bit,or group of bits, of the coded sequence to a different one of modulators310. Spatial multiplexer 308 may additionally include one or moreinterleavers to interleave the digital sequence prior to providing the Mseparate sequences. In some embodiments, multiplexer 308 may provide oneor more bits of the coded sequence to more than one modulator. Theparticular sequence provided to one of the M modulators may be referredto as a transmit “stream.”

Transmitter 302 can include M modulators, shown in FIG. 3 as modulators310, for modulating the M separate sequences provided from spatialmultiplexer 308. Each of modulators 310 preferably modulates its inputdigital sequence to a signal suitable for transmission through channelH. Each modulator preferably is associated with one of transmitteroutputs TX_1, . . . , TX_M. Thus, each of modulators 310 preferablyprovides one of the M output signals of transmitter 302. In someembodiments, such as in the wireless transmission system of FIG. 2,transmitter outputs TX_1, . . . , TX_M may be transmit antennas. Thus,for convenience, a transmitter output may be referred to as a transmitantenna. However, this is not intended to limit the present invention inany way.

With continuing reference to FIG. 3, modulators 310 preferably groupbits of digital information into symbols of one or more bits. Modulators310 preferably modulate these symbols to signals by adjusting themagnitude, phase, or a combination of magnitude and phase, of a suitablecarrier signal. The carrier signal may have any suitable frequency(e.g., baseband signal, passband signal, etc.) and any suitablecharacteristics (a non-return-to-zero (NRZ) signal, etc.). The magnitudeand phase of a signal associated with a particular symbol can beillustrated by a signal point in a signal constellation set representingthe modulation scheme. Thus, to transmit a symbol, the carrier signalcan be adjusted to the magnitude and phase of the signal pointassociated with that symbol for a period of time referred to as a“symbol period.”

Referring now to FIGS. 4A-4B, illustrative signal constellation sets,corresponding to various modulation schemes, are shown that may be usedby a modulator (e.g., each of modulators 310 of FIG. 3). Referring firstto FIG. 4A, a one-dimensional (1D) signal constellation set is shownthat may correspond to a pulse amplitude modulation (PAM) schemes. PAMschemes can transmit different symbols by varying the amplitude (e.g.,voltage magnitude) of a carrier signal. FIG. 4A shows an 8-PAM signalconstellation set for mapping three-bit symbols into eight signalamplitudes. One particular symbol-to-signal point mapping is shown inFIG. 4A. Using this particular mapping, symbol “001” may be transmittedwith a signal magnitude of +a/7. However, it should be understood thatthis mapping is merely illustrative, and any other one-to-one mappingmay be used.

Referring now to FIG. 4B, an illustrative two-dimensional (2D) signalconstellation set is provided that can be used by a modulator (e.g.,each of modulators 310 of FIG. 3) to modulate symbols. The 2Dconstellation set of FIG. 4B is shown on a complex number plane, wherethe horizontal axis represents real values and the vertical axisrepresents imaginary values. The illustrated constellation set maycorrespond to a quadrature amplitude modulation (QAM) scheme with 16signal points, referred to as a 16-QAM modulation scheme. Each symbolcan include four bits, and each symbol can be modulated to a signalpoint having a different amplitude, phase, or a combination of amplitudeand phase. For example, using the shown symbol-to-signal point mapping,symbol “0110” can be modulated to an amplitude given by distance r andphase θ. However, it should be understood that this mapping is merelyillustrative, and any other one-to-one mapping may be used.

For convenience, the number of total signal points in a constellationset will hereinafter be defined by a variable, Q. Thus, FIG. 4A shows aconstellation set where Q=8, and FIG. 4B shows a constellation set whereQ=16.

Referring back to FIG. 3, modulators 310 can use any suitableone-dimensional or multi-dimensional modulation scheme, and thereforeany suitable signal constellation set, for converting symbols tosignals. For example, modulators 310 may modulate symbols according tothe 8-PAM or 16-QAM illustrated in FIGS. 4A and 4B, respectively. Inother embodiments, modulators 310 can modulate symbols based on phaseshift keying (PSK), frequency shift keying (FSK), or any other suitablemodulation scheme.

It should be understood that transmitter 302 of FIG. 3 is merelyillustrative. In particular, any of the components of transmitter 302may be modified, omitted, or rearranged, or any additional componentsmay be added, without departing from the scope of the invention.Furthermore, any of the components may be combined into a component withcombined functionality.

With continuing reference to FIG. 3, receiver 304 can receive aplurality of signals from channel H. In particular, receiver 304 caninclude receiver inputs RX_1, . . . , RX_P for obtaining P signals fromchannel H. In some embodiments, such as in the wireless communicationssystem of FIG. 2, inputs RX_1, . . . , RX_P may be receiving antennas.Thus, for convenience these inputs may be referred to as antennas.However, this is not intended limit the present invention in any way. Insome embodiments, channel H may allow a signal transmitted from aparticular transmit antenna, TX_i, to reach multiple receive antennas.Thus, the signal received by a given receiver antenna may be based onsignals from multiple transmit antennas. In particular, a signalreceived by a receive antenna, RX_i, may be a linear combination of thesignals provided by transmit antennas TX_1, . . . , TX_P. Thus, inmatrix form, system 300 can be modeled by the equations,y _(P×1) =H _(P×M) x _(M×1) +n _(P×1), and  (1)

$\begin{matrix}{\begin{bmatrix}y_{1} \\\vdots \\y_{P}\end{bmatrix} = {{\begin{bmatrix}h_{11} & \ldots & h_{1,M} \\\vdots & \ddots & \vdots \\h_{P\; 1} & \ldots & h_{PM}\end{bmatrix}\begin{bmatrix}x_{1} \\\vdots \\x_{M}\end{bmatrix}} + {\begin{bmatrix}n_{1} \\\vdots \\n_{P}\end{bmatrix}.}}} & (2)\end{matrix}$Here, equation (2) is an expanded version of equation (1), where y_(P×1)is a P-dimensional signal vector representing the signals received bythe P inputs of receiver 304, J_(P×M) is a P×M matrix representing theeffect of channel H, x_(M×1) is a M-dimensional signal vectorrepresenting the signals transmitted by the M outputs of transmitter302, and n_(P×1) is a P-dimensional signal vector representing anyadditive noise affecting the transmitted signals. Thus, for example, thesignal received by the first of inputs 314 may equal,y ₁ =h ₁₁ x ₁ + . . . +h _(1m) x _(M) +n _(′1),  (3)a weighted addition of symbols from transmit antennas TX_1, . . . ,TX_M. In some embodiments of the present invention, receiver 304 maymodel each noise component of n_(P×1) (e.g., n_(′1)) as additive whiteGaussian (AWG), where the noise at an input TX_i may not be correlatedto the noise at any of the other inputs. However, the present inventionis not limited to this particular embodiment.

Thus, with continuing reference to FIG. 3, to interpret the informationtransmitted by transmitter 302, receiver 304 can attempt to demultiplexthe noisy, altered signals received by RX_1, . . . , RX_P to obtain theoriginal M independent streams. To perform this complex task, receiver304 can include MIMO detector 312 and decoder 314. MIMO detector 312 canbe a detector that interprets the P received streams to estimate the Mtransmitted streams. Detector 312 can estimate the transmitted sequencesusing a suitable detection algorithm. As will be described in furtherdetail in connection with FIG. 5, detector 312 preferably performsdetection based on the K-Best algorithm.

Decoder 314 of receiver 304 can decode the streams from MIMO detector312 into an estimate of the originally transmitted or played backinformation (e.g., digital information 104 of FIG. 1). Decoder 314 maybe a decoder based on the error correcting or detecting code used byencoder 306. For example, if encoder 306 is an LDPC encoder, decoder 314may be an LDPC decoder. In this way, decoder 314 can remove theredundancy added by encoder 316, and can correct or detect any errorsthat may remain after signal detection.

Referring now to FIG. 5, tree diagram 500 is shown that can illustrateeach possible combination of symbols transmitted by a MIMO transmitter(e.g., transmitter 302 of FIG. 3). In particular, tree diagram 500 canillustrate the different combinations of symbols provided by antennasTX_1, . . . , TX_M for a particular symbol period. Tree diagram 500preferably includes M levels—one level for each transmitter output,TX_i. Each node in tree diagram 500 preferably has Q children branches,where each branch corresponds to a possible symbol in the correspondingmodulation scheme. Thus, for the 8-PAM scheme of FIG. 4A, each node mayhave eight children branches for symbols “000,” . . . , “100.” For the16-QAM scheme of FIG. 4B, each node may have 16 children branches foreach four-bit symbol.

With continuing reference to FIG. 5, tree diagram 500 preferably isorganized such that the Mth transmitter output, TX_M, is shown as thetop level of the tree. The Q branches for TX_M preferably lead tochildren nodes at the next level, which preferably corresponds to the(M−1)th transmitter output, TX_(M−1). Then, Q branches preferably leadaway from each of these nodes to the (M−2)th transmitter output level.For simplicity, the level in tree diagram 500 corresponding to the ithtransmitter output, TX_i, will hereinafter be referred to as the ithlevel. Thus, at each level i of tree diagram 500, every possiblecombination of symbols for transmitter outputs i, . . . , M can beillustrated. For example, at the (M−2)th level, each combination ofsymbols for transmitter outputs TX_M, TX_(M−1), and TX_(M−2) is shown,for a total of Q³ symbol combinations. For the 8-PAM scheme of FIG. 4A,there may be a total of 512 branches at this level, and for the 16-QAMscheme of FIG. 4B, there may be a total of 4096 branches at this level.

A particular detection algorithm used by a MIMO detector (e.g., detector316 of FIG. 3) may find one or more paths through tree diagram 500. Thatis, to interpret information received from a channel, the detector mayfind one or more paths through tree diagram 500 that are likely tocorrespond to the path actually transmitted. For example, amaximum-likelihood detection algorithm can be used to find the mostprobable path through the tree diagram. To accomplish this,maximum-likelihood detection may involve finding the likelihood of eachpossible path through tree diagram 500. Because a total of Q^(M) pathsmay exist in tree diagram 500, for MIMO systems with a large M, a largenumber Q, or large M and large Q, maximum-likelihood detection may betoo complex to implement in practical detectors. Thus, the presentinvention preferably uses a less complex, yet still high performance,detection algorithm referred to as the K-Best detection algorithm. Forillustrative purposes, the K-Best detection algorithm will be describedbriefly here.

With continuing reference to FIG. 5, a detector (e.g., MIMO detector 316of FIG. 3) can systematically process a set of received signals usingtree diagram 500 one level at a time. Detection for a particular symbolperiod can terminate once the signals have been processed for eachlevel. Starting at the Mth (top) level, the detector can determine whichK branches most likely correspond to the actual symbol transmitted byTX_M. In particular, based on the P received signals, the detector candetermine the most probable K, or “K best,” symbols transmitted by TX_M.If the channel matrix is upper triangular, or can be made uppertriangular, the detector can base its decision on only the Pth detectorinput, RX_P. This preferably results in significant complexity savings,as only one, rather than up to P, receiver inputs can be considered. Inmatrix form, if the channel matrix can be converted to a format givenby,B _(P×1) =R _(P×M) x _(M×1) +n _(P×1), or  (4)

$\begin{matrix}{{\begin{bmatrix}B_{1} \\\vdots \\B_{P}\end{bmatrix} = {{\begin{bmatrix}r_{11} & \ldots & \ldots & r_{M\; 1} \\0 & r_{22} & \ddots & \vdots \\\vdots & \ddots & r_{33} & \vdots \\0 & \ldots & 0 & r_{MM}\end{bmatrix}\begin{bmatrix}x_{1} \\\vdots \\x_{M}\end{bmatrix}} + \begin{bmatrix}n_{1} \\\vdots \\n_{P}\end{bmatrix}}},} & (5)\end{matrix}$then the Pth receiver input signal, B_(P), may be given by,B _(P) =r _(MM) x _(M) +n _(P).  (6)Note that, for simplicity, the R matrix in equation (5) is assumed to besquare (e.g., M=P). However, this is merely illustrative.

With continuing reference to FIG. 5, after K best branches have beenselected from Mth (top) level processing, the resulting K nodes can beconsidered in the next, (M−1)th level. Thus, this level (and eachsuccessive level) can choose the best K branches from K×Q possiblebranches. For example, assume K=2 and the 8-QAM scheme illustrated inFIG. 4A. Also assume the two nodes shown in region 502 represent the Kbest selections from Mth level processing. Then, K=2 branches can bechosen based on the K×Q=2×8=16 branches leading away from region 502. Asillustrated in equation (6), if the channel matrix is upper triangular,or can be made upper triangular, the decision for the (M−1)th level, andtherefore the estimate for TX_(M−1) and TX_M, can depend on onlyRX_(M−1) and RX_M. As the Mth receiver input may have taken care of thepossibilities for the Mth transmitter output in the Mth levelprocessing, the (M−1)th level processing can be based substantially onthe RX (M−1) input. Thus, processing received signals with tree diagram500 can continue in the above described manner, where the ith level canbe processed based substantially on the RX_i input.

It should be understood that the description of the K-Best algorithmdescribed above is merely illustrative, and is not meant to limit thepresent invention in any way. In fact, some embodiments of the inventioncan be used with other detection algorithms, such as maximum-likelihooddetection, sphere decoding, zero-forcing decoding, or a modified orcombined version of any of these detection algorithms. This will bediscussed in greater detail below.

Note that tree diagram 500 assumes that any combination of symbols maybe possible for the M transmitter outputs. It should be understood,however, that tree diagram 500 may be modified if certain combinationsof symbols are not possible. For example, an error control code may beimplemented by, for example, encoder 306 that prevents particularcombinations of symbols to be transmitted concurrently from TX_1, . . ., TX_M. In these embodiments, tree diagram 500 can be modified to removepaths that represent these particular combinations of symbols.

Referring now to FIG. 6, illustrative MIMO detector 600 is shown thatcan implement a K-Best detection algorithm in accordance with anembodiment of the present invention. MIMO detector 600 can include aplurality of K-Best (KB) stages 604 a-604 c, which may be pipelinedstages, and soft output computation unit 606. The variable, N,hereinafter refers to the number of KB stages included in a MIMOdetector (e.g., MIMO detector 600) of the present invention. The valueof N preferably is based on the number of transmit antennas, M, andtherefore preferably is based on the number of levels in an associatedtree diagram (e.g., tree diagram 500 of FIG. 5). Detector 600 preferablyhas a scalable architecture, as detector 600 can be altered by simplyadding or removing detector stages 604 if a different sized MIMO systemis desired.

With continuing reference to FIG. 6, in some embodiments of the presentinvention, N may equal the number of transmit antennas, M. In theseembodiments, each KB stage may be associated with a level of aparticular tree. That is, KB, may perform the processing described aboveassociated with the ith level of the tree diagram. If the receivedsignals are obtained from an upper triangular channel, stages KB₈, . . ., KB₁ may process received signals from receive antennas RX_8, . . . ,RX_1, respectively. Other embodiments of detector 600 will be describedin greater detail below.

Thus, except for the Nth stage, each KB stage of detector 600 preferablyprovides K outputs for a next detector stage based on the K inputs of aprevious detector stage. For a given stage 1, the K inputs preferablycorrespond to K nodes in the ith level of a tree diagram (e.g., treediagram 500 of FIG. 5) Similarly, the K outputs preferably correspond toK branches leading away from the K nodes in the ith level. These Kbranches preferably lead to K nodes, or inputs, in the next level. Thus,for convenience and where appropriate, an input of a detector stage maybe referred to as a “parent node,” where each parent node is associatedwith a particular combination of transmit symbols. Also, an output of adetector stage may be referred to as a “branch,” where the branch may beassociated with the parent node of a next detector stage. As stagesKB_(N), . . . , KB₂ produce branches that are used by another stage, anyprocessing performed by these stages may be referred to as“intermediate.” Similarly, as stage KB₀ preferably provides an outputfrom the detector stages, this stage is preferably referred to as afinal stage.

With continuing reference to FIG. 6, soft output computation unit 606 ofdetector 600 can compute soft information. Unit 606 can compute softinformation using information (e.g., probabilities) on the K best pathsprovided by KB stages 605 a-605 c. In some embodiments, unit 606 maycalculate soft information in the form of log-likelihood ratios (LLR).In particular, soft output computation unit 606 may provide an outputwhere the sign of the output indicates the most likely value of the bit(e.g., most likely ‘1’ if positive and most likely ‘0’ if negative) andthe magnitude of the output indicates the confidence or strength of thedecision. This may be provided to a soft decoder (e.g., decoder 314 ofFIG. 3), which performs decoding based on soft information.

As described above in connection with FIG. 5, the K-Best detectionalgorithm preferably operates on signals received from a channel, wherethe channel can be modeled as an upper triangular matrix. Because thechannel (e.g., channel H of FIG. 3) can be a full non-zero matrix, thechannel can be triangularized by preprocessor 602. Preprocessor 602preferably converts a full channel matrix, H, into an upper triangularmatrix, R. In some embodiments, preprocessor 602 may triangularize thechannel matrix using QR decomposition. That is, preprocessor 602 maydecompose channel matrix H into an orthogonal, unitary matrix Q and anupper triangular matrix R, where H=QR. Preprocessor 602 may implement QRdecomposition using Gram-Schmidt orthogonalization or another suitableimplementation. In other embodiments, preprocessor 602 may triangularizethe channel matrix using Cholesky factorization. That is, preprocessor602 may decompose channel matrix H such that H=LL*, where L is lowertriangular and L* is the upper triangular, conjugate transpose of L.Preprocessor 602 may implement Cholesky factorization using the Choleskyalgorithm or using any other suitable implementation.

Referring now to FIG. 7, illustrative detector stage 700 is shown forobtaining the K best symbols for a particular transmit antenna, i, inaccordance with an embodiment of the present invention. Detector stage700 may be a more detailed, yet still simplified, view of one of stages604 in detector 600 (FIG. 6). Thus, detector stage 700 may have any ofthe features and functionalities of any of stages 604. Detector stage700 preferably includes common term computation unit 702, enumerationunit 704, and merge sort unit 706, each of which will be described ingreater detail below.

Common term computation unit 702 preferably processes a received signal,y_(i), where y_(i) preferably is received from the RX_i antenna of aMIMO receiver. The resulting processed signal may be referred to assignal B_(i). In some embodiments, computation unit 702 can process theith received signal based on the K best outputs from a previous detectorstage. That is, if stage 700 corresponds to the kth KB stage in FIG. 6,computation unit 702 can process the received signal based on theoutputs of the (k+1)th KB stage. This input is illustrated asHist_(M, . . . i) in FIG. 7. In some embodiments, computation unit 702can process the received signal differently for each of the K bestoutputs from the previous stage. The processing preferably reduces theinterference from other transmit antennas such that the value of theresulting processed signal depends substantially on the ith transmitantenna, TX_i. That is, assuming a particular combination of symbolsfrom transmitter TX_(i+1), . . . , TX_M derived from a parent node,computation unit 702 preferably reduces the effect of these symbols onRX_i. As there can be K parent nodes, for convenience, the processedsignal for the ith received signal and the jth parent node may bedefined by the symbol, B_(i,j) Computation unit 702 can process thereceived signal for these different outputs in parallel or sequentially.

Common term computation unit 702 can also process received signal y_(i)based on the preprocessing performed on its corresponding channel matrix(e.g., by channel preprocessor 602 of FIG. 6). Otherwise, detector stage700 may attempt to detect y_(i) assuming an associated channel matrix ofR, rather than its actual channel matrix of H. Thus, computation unit702 may process the received signal such that processed signal B_(i) mayappear to be a signal received from channel R. That is, computation unit702 may convert y_(i), where

$\begin{matrix}{{y_{i} = {{\sum\limits_{j = 1}^{M}{h_{ij}x_{M}}} + n_{M}}},{into},} & (7) \\{B_{i} = {{\sum\limits_{j = i}^{M}{r_{ij}x_{M}}} + {n_{M}.}}} & (8)\end{matrix}$Detector stage 700 can then properly detect the processed signalassuming an associated channel matrix of R. In other embodiments of thepresent invention, processing the received signal based on R can beperformed by a processor external to detector stage 700. In fact,processing based on R can be performed using a single external processorfor multiple y_(i)s, rather than separately with a processor in eachstage. In this way, processing based on R may be completed even beforestarting detection. For example, referring back to FIG. 6, processingbased on R can be performed in preprocessor 602 or in a separateprocessor coupled to preprocessor 602 (not shown).

Referring now to FIG. 8, the operation of common term computation unit702 can be illustrated in accordance with an embodiment of the presentinvention. Signal constellation set 800 can include four signal points.Thus, constellation set 800 can be a complete 4-PAM constellation set ora subset of a larger signal set. The signal points of signal set 800preferably correspond to the expected values of B_(i,j) or E[B_(i,j)],for different possible symbols transmitted from antenna TX_i, assumingparent node j (e.g., a particular combination of symbols transmittedfrom antennas TX_(i+1), . . . , TX_M). Thus, to compare signal y_(i)with the constellation points of set 800, common term computation unit702 can process signal y_(i) using any of the techniques describedabove. The resulting processed signal, B_(i,j), can map ontoconstellation set 800 at some point relative to the other signal points.For the example of FIG. 8, B_(i,j) can map onto a point between therightmost two signal points of constellation set 800. B_(i,j), however,can map onto any point on the line graph in FIG. 8, as there may be anydegree of noise (e.g., n_(i) in equation (2)) affecting the receivedsignal.

Referring back to FIG. 7, common term computation unit 702 can output aprocessed signal, B_(i,j). Using this processed signal, enumeration unit704 preferably computes a plurality of metrics. In some embodiments, fora given B_(i,j), enumeration unit 704 may compute a metric associatedwith each signal point in a constellation set. Enumeration unit 704 cancompute metrics based on the distance of the processed signal to eachsignal point in the constellation set. For example, in FIG. 8,enumeration unit 704 can compute four metrics, where the metrics arebased on distances D₁, D₂, D₃, and D₄. These distances may be Euclideandistances or any other suitable distance calculation. Thus, in someembodiments, enumeration unit 704 can compute metrics according to∥B_(i)−R_(ii)C_(j)∥². In other embodiments, to prevent from having toimplement a square function (e.g., a multiplier), enumeration unit 704can compute an approximate linear metric according to|B_(i)−R_(ii)C_(j)|. In still other embodiments, and formultidimensional signal constellation sets, enumeration unit 704 maycompute an approximate linear metric according to |Im{B_(i)−R_(ii)C_(j)}|+|Re{B_(i)−R_(ii)C_(j)}|. For convenience, thecollection of metrics (e.g., D₁, D₂, D₃, and D₄ in FIG. 8) computed byan enumeration unit (e.g., enumeration unit 704) for processed signalB_(i,j) will hereinafter be represented by a symbol, {D}_(i,j).

Enumeration unit 704 of FIG. 7 can compute a set of metrics for each ofthe K best symbols from a previous detector stage. That is, for eachB_(i,j), enumeration unit 704 can compute a set of metrics, {D}_(i,j).Thus, for each symbol period, enumeration unit 704 preferably computes aplurality of metrics corresponding to {D}_(i,1), . . . {D}_(i,K), orsimply {D}_(i). In some embodiments of the present invention,enumeration unit 704 may include multiple copies of any suitablecomputational circuitry or structures to compute each set of metrics inparallel. In other embodiments, enumeration unit 704 may reusecomputational structures by computing each set sequentially. Computingthe sets in series may be advantageous if computation unit 702 providesthe processed signals, B_(i,j), in series.

With continuing reference to FIG. 7, merge sort unit 706 can find the Kbest signals based on the K×Q metrics {D}_(i) provided by enumerationunit 704. Merge sort unit 706 preferably sorts the metrics of {D}_(i)into an ascending or descending order. Because a smaller metric mayindicate a more probable symbol, merge sort unit 706 can choose Ksymbols with the smallest metrics. That is, depending on whether anascending or descending order of metrics is achieved, merge sort unit706 can select the first or last K metrics as the best K branches. Mergesort unit 706 may provide these K best branches to the (i−1)th detectorstage. If the current detector stage is the final stage in a detector(e.g., KB₀ in FIG. 6), merge sort unit 706 may provide the K bestbranches to a soft or hard output generator (e.g. soft outputcomputation unit 606 in FIG. 6).

Referring now to FIG. 9A, illustrative merge sort unit 900 is shown inaccordance with an embodiment of the present invention. Merge sort unit900 may be a more detailed, yet still simplified, embodiment of mergesort unit 706 in FIG. 7. Merge sort unit 900 can include parent nodesorting unit 902 and K-Best sorting unit 904, each of which will bedescribed in greater detail below.

Parent node sorting unit 902 preferably sorts metrics for a given parentnode. That is, parent node sorting unit 902 can sort metrics {D}_(i,j)associated with antenna RX_i and parent node j. Thus, sorting unit 902may sort up to Q metrics for the Q possible symbols associated withparent node j. Sorting unit 902 preferably sorts these metrics using amerge sorting algorithm. The merge sorting algorithm may enable some orall of the metrics to be sorted substantially concurrently. Thus, mergesort unit 902 preferably has high throughput. If a hardwareimplementation is used, merge sort unit 902 may sort the metrics withina clock cycle. In other embodiments, parent node sorting unit 902 caninclude a plurality of subunits for performing intermediate-sized mergesorts. For hardware implementations, some of these subunits may operatein a single clock cycle or may be pipelined.

In some embodiments, parent node sorting unit 902 may receive each setof metrics, {D}_(i,j), for the K parent nodes sequentially. That is,sorting unit 902 may receive sets {D}_(i,K), . . . , {D}_(i,1) one setat a time. In these embodiments, sorting unit 902 preferably providesthese sets at its output, sorted by value. In particular, at each timeinterval (e.g., clock cycle), parent node sorting unit 902 may provide adifferent set of sorted metrics. Thus, sets of sorted information can beprovided to K-Best sorting unit 904 sequentially.

With continuing reference to FIG. 9A, K-Best sorting unit 904 is shownfor combining and sorting the sequentially provided sets of sortedmetrics from parent node sorting unit 902. In particular, K-Best sortingunit 904 preferably sorts K best metrics computed thus far with a newset of sorted metrics from parent node sorting unit 902. For example,after a given time, K-Best sorting unit 904 may have combined and sortedmetrics for TX_(i+1), . . . , TX_M. At the next time interval, thesorted metrics for these transmit antennas may be sorted with the sortedmetrics for TX_i provided by parent node sorting unit 902. Thus, afterthis next time interval, K best sorting unit 904 preferably provides theK best metrics for antennas TX_i, . . . , TX_M. This result may again beused as next set of sorted metrics provided by parent node sorting unit902. Therefore, to continually update its K best metrics, the output ofK-Best sorting unit 904 may be fed back as an input via feedback path906. In some embodiments, feedback path 906 may additionally include abuffer for storing the current K best paths, such that these K bestpaths may be available for sorting with a new set of sorted metrics.

Referring now to FIG. 9B, merge sort unit 920 is provided for sortingthe metrics of a Q=4 signal constellation set in accordance with anembodiment of the present invention. Merge sort unit 902 may be aparticular embodiment of merge sort unit 900 of FIG. 9A for Q=4 and K=3.Thus, merge sort unit 902 may be used with a 4-PAM modulation scheme, orany other modulation scheme with four signal points, and can be used tosort metrics based on D₁, D₂, D₃, and D₄, described above in connectionwith FIG. 8. Merge sort unit 920 preferably includes 2×2 merge sort unit922 and 3×3 merge sort unit 924, which will each be described in detailbelow.

In FIG. 9B, 2×2 merge sort unit 922 preferably has four inputscorresponding to metrics based on D₁, D₂, D₃, and D₄. In one (or more)clock cycles, 2×2 merge sort unit 922 preferably sorts these metricsusing a merge sorting algorithm. Because only K total metrics mayeventually be provided, 2×2 merge sort unit 922 may only output the K=3best (e.g., lowest) metrics. The result of the highest metric ispreferably discarded. Thus, in some embodiments, 2×2 merge sorting unit922 may provide sorted metrics for the K=3 best of D₁, D₂, D₃, and D₄for each of M transmit antennas in successive time intervals.

With continuing reference to FIG. 9B, 3×3 merge sort unit preferablyprovides the K best outputs for a current stage. In particular, 3×3merge sort unit 924 may sort the sorted metrics provided by 2×2 mergesort unit 922. As only K=3 final metrics may be needed, 3×3 merge sortunit 924 preferably discards any metrics that do not fall within thebest K metrics. The K retained metrics may then be compared to the anext set of sorted metrics, where only the best three of six metricspreferably are retained. In some embodiments, 3×3 merge sort unit 924may perform the 3×3 merge sort in a single clock cycle. Thus, inembodiments where both 2×2 merge sort unit 922 and 3×3 merge sort unit924 complete their respective sorts in a single clock cycle, only onepipelined stage may be needed. This pipelined stage is illustrated inFIG. 9B as a dotted line.

Examples of the present invention provided thus far have focusedprimarily on real (e.g., PAM) signal sets. It should be understood thatany of the above described techniques and examples may be extendeddirectly to complex (e.g., QAM) signal sets. In other embodiments,rather than considering a QAM signal set to be within a complex domain,the signal set may be modeled within the real domain. In particular, theM-dimensional complex signal model for the M transmit antennas may beconverted into a 2M-dimensional real signal model according to

$\begin{matrix}{\begin{bmatrix}{{Re}\left\{ y \right\}} \\{{Im}\left\{ y \right\}}\end{bmatrix} = {{\begin{bmatrix}{{Re}\left\{ H \right\}} & {{- {Im}}\left\{ H \right\}} \\{{Im}\left\{ H \right\}} & {{Re}\left\{ H \right\}}\end{bmatrix}\begin{bmatrix}{{Re}\left\{ c \right\}} \\{{Im}\left\{ c \right\}}\end{bmatrix}} + {\begin{bmatrix}{{Re}\left\{ n \right\}} \\{{Im}\left\{ n \right\}}\end{bmatrix}.}}} & (9)\end{matrix}$This conversion may result in significant throughput gains, as thenumber of total number of signal points in the constellation set isreduced considerably. For the 16-QAM signal set shown in FIG. 4B,equation (9) produces an 8-dimensional signal vector, as each originaldimension is reduced to four signal points (a square root decrease), andthe number of real dimensions double (a linear increase). Thus, althoughthe number of real dimensions increase, the increase is significantlyless than the decrease in the number of signal points per dimension.

In some embodiments of the present invention, a complex signal set(e.g., QAM) may be implemented by doubling the number of detectorstages. In particular, each transmit antenna may be associated with twodetector stages—one stage to process Re{y_(i)} and the other to processIm{y_(i)}. Thus, a detector such as detector 600 in FIG. 6 may includeup to double the number of stages, N, as number of transmit antennas, M.In comparison to a detector with stages that process y_(i), thecomplexity of each stage preferably is considerably reduced, while thenumber of stages preferably increases linearly. The throughput of thedetector can be significantly decreased if each of stage can more thanhalve the amount of processing time. For the 16-QAM described above inconnection with FIG. 4B, the number of stages preferably increases fromM to 2M stages, while the number of signal points considered by eachstage preferably is reduced from 16 to four. Thus, in some embodiments,each stage of the 16-QAM MIMO detector may be similar to a stage for a4-PAM MIMO detector. That is, each stage of the 16-QAM MIMO detector mayinclude components similar to those illustrated above in connection withFIGS. 8 and 9B.

Referring now to FIG. 10, an illustrative block diagram of detectorstage 1000 for reducing the complexity of detection is shown inaccordance with an embodiment of the present invention. Detector stage1000 preferably includes common term computation unit 1002, signal setreduction unit 1004, enumeration unit 1006, and merge sort unit 1008.Detector stage 1000 has many similar components as detector stage 700 ofFIG. 7. In particular, detector stages 700 and 1000 both include acommon term computation unit, an enumeration unit, and a merge sortunit. Therefore, the description of detector stage 1000 will be keptbrief and will focus primarily on differences between detector stage 700and detector stage 1000. However, it should be understood that any ofthe features and functionalities of detector stage 700 can be includedin detector stage 1000.

With continuing reference to FIG. 10, signal set reduction unit 1004 canreduce the number of signal constellation points considered by thedetector stage. In particular, reduction unit 1004 can find a subset ofthe total signal constellation point by removing constellation pointsthat are very unlikely to correspond to the actually transmitted signalpoint. In some embodiments, reduction unit 1004 may divide the possiblevalues of a processed received signal from common term computation unit1002 into a plurality of regions. The subset of signal points chosen maydepend on the particular region that a processed received signal mapsinto. In this way, the complexity of enumeration unit 1006 can bereduced, as it preferably does not calculate as many metrics.Additionally, the complexity, and possibly the throughput, of merge sortunit 1008 can be reduced, as fewer metrics preferably are sorted. Thevariable, C, will hereinafter define the total number of signalconstellation points chosen by signal set reduction unit 1004.

Referring now to FIGS. 11A-11B, an illustration of the regions of signalset reduction unit 1004 (FIG. 10) is shown. FIG. 11A illustratespossible regions for a dimension of a 16-QAM signal set (described abovein connection with FIG. 4B), and FIG. 11B illustrates possible regionsfor a dimension of a 64-QAM signal set. The number of regions may dependon the number of total signal points in the constellations set. Forexample, the four-point real constellation set of a 16-QAM scheme mayhave three regions, identified in FIG. 11A as regions A, B, and C, whilethe eight-point real constellation set of a 64-QAM may have fiveregions, identified in FIG. 11B as regions A, B, C, D, E. However, itshould be understood that any suitable number of regions may be used forthese regions, and their boundaries may be at any suitable location onthe line graphs.

With continuing reference to FIGS. 11A and 11B, the particular symbolschosen by a signal set reduction unit (e.g., reduction unit 1004 of FIG.10) preferably depend on the region that an input signal maps into. Theinput signal may be a processed signal from a common term computationunit (e.g., common term computation unit 1002 of FIG. 10). Thus, theresulting symbols chosen by the reduction unit may correspond roughly toa set of symbols with constellation points within or closest to theregion associated with an input signal. For example, referring to FIG.11B, if an input signal with a value in region D is chosen, each symbolexcept for “000” and “001” may be selected. In some embodiments, thesubset of signal points for each region may be selected using a tablelookup or other suitable selection mechanism. It should be understoodthat a signal set reduction unit (e.g., reduction unit 1004 of FIG. 10)can output any suitable number of signal points. To improve theperformance of the detector stage, a large number of signal points maybe selected. To improve the throughput or to decrease the complexity ofthe system, a fewer number of signal points may be selected.

Referring now to FIG. 12, illustrative 64-QAM MIMO detector stage 1200is shown that utilizes several of the above described techniques forimplementing a detector stage in accordance with an embodiment of thepresent invention. Detector stage 1200 can include common termcomputation unit 1202, signal set reduction unit 1204, enumeration unit1206, 2×2 merge sort unit 1208 a, 1×1 merge sort unit 1208 b, 4×2 mergesort unit 1210, and 5×5 merge sort unit 1212. Any of these componentsmay have some or all of the features or functionalities of componentswith similar names discussed above. As illustrated in FIG. 12, detectorstage 1200 is implemented with Q=8 (a dimension of 64-QAM), C=6, andK=5.

Common term computation unit 1202 may provide a processed signal basedon a received signal, an upper triangular channel matrix, andinformation from previous detector stages using any of the techniquesdescribed above. That is, using this information, computation unit 1202may provide a processed signal that can map onto a line graph with theQ=8 signal points of the corresponding signal constellation set. Fromthe processed signal, signal set reduction unit 1204 can select a subsetof the Q constellation points. As illustrated in FIG. 12, reduction unit1204 can select six of the eight signal points to consider in theremainder of the detector stage. Then, enumeration unit 1206 preferablycomputes distance-based metrics for each of the six symbols selected bysignal set reduction unit 1204.

With continuing reference to FIG. 12, the remaining components followingenumeration unit 1206 preferably sort the metrics computed byenumeration unit 1206. As indicated by the dotted lines, detector stage1200 calls for three pipelined stages to complete the merge sort. In thefirst stage, the six unsorted metrics provided by enumeration unit 1206can be split and sorted separately. In particular, 2×2 merge sort unit1208 a can sort four of the six metrics, and 1×1 merge sort unit 1208 bcan sort the other two metrics. The two results can be combined andsorted by the 4×2 merge sort unit 1210 in the next pipelined stage.Because K=5, 4×2 unit 1210 preferably provides only the five bestmetrics of the six sorted metrics. These five best metrics preferablycorrespond to a particular parent node. Thus, in the final pipelinedstage, 5×5 merge sort unit 1212 preferably combines and sorts the fivebest metrics for this parent node with the five best metrics from otherparent nodes that have already been processed. From this sort, a newbest five metrics may be retained, and can be used again when five moresorted metrics are provided for a next parent node. Because groups ofmetrics can be sorted within a single pipelined stage, detector stage1200 can have an throughput improvement of up to five times thethroughput of a detector stage that sorts metrics sequentially. Thiswould be a significant throughput improvement and may be highlyadvantageous for systems that desire high-speed processing.

Referring now to FIG. 13, illustrative flow diagram 1300 is shown forprocessing signals received from a MIMO system in accordance with anembodiment of the present invention. At step 1302, a plurality ofsignals may be received from a MIMO transmission scheme. The pluralityof signal may be received from a plurality of receive antennas. Theplurality of signals may have been transmitted according to a modulationscheme, such as a PAM, QAM, or PSK transmission scheme. In addition,each of these signals may include information about each of the signalstransmitted from the plurality of transmit antennas. That is, eachreceived signal may be a weighted addition of the signals transmitted bythe transmit antennas with any additive noise. The way in which thetransmit signals are altered prior to reception by the plurality ofreceive antennas may be represented by a channel response matrix, H.

At step 1304, the channel response matrix associated with the pluralityof received signals can be preprocessed. The channel matrix can bepreprocessed to form a new, upper triangular matrix, R. This can beaccomplished using QR decomposition, Cholesky factorization, or anyother suitable triangularization technique. Then, at step 1306, areceived signal can be processed based on R, as well as on the K parentnodes obtained from processing another of the received signals. Thus,each of the received signals can be processed in stages. The receivedsignals may be processed such that the ith processed signal may besubstantially based on the ith transmit antenna, the processed signalmay correspond to a channel response matrix of R.

From the processed received signals, a predetermined number of likelycombinations of transmit symbols are determined at step 1308. That is, Klikely combinations of symbols are chosen from the many possiblecombinations of transmitted symbols. These predetermined combinationscan be found using a K-best detection algorithm, which implements abreath-first search through a tree diagram (e.g., tree diagram 500 ofFIG. 5) representing each combination of possible transmit symbols.Thus, at step 1310, soft information may be computed for each bittransmitted from the plurality of transmit antennas. The softinformation may be computed based on the likely combinations of transmitsymbols, and based on how likely each of these combinations are. Thesoft information may be in the form of log-likelihood ratios (LLRs).Finally, if the received information is in a coded form, the softinformation can be decoded at step 1312. Thus, the result of decodingpreferably corresponds to the information conveyed by a source.

It should be understood that flow diagram 1300 of FIG. 13 is merelyillustrative. Any of the steps in flow diagram 1300 may be modified,rearranged, or omitted, and any additional components may be added,without departing from the scope of the present invention.

Referring now to FIG. 14, illustrative flow diagram 1400 is shown forfinding a predetermined number of likely symbols for a symbol period inaccordance with an embodiment of the present invention. In someembodiments, the steps of flow diagram 1400 can be incorporated intostep 1308 of FIG. 13. At step 1402, a plurality of metrics can becomputed for each transmitted signal. Each of the plurality of metricspreferably is associated with a possible symbol in a signalconstellation set representing the modulation scheme used. Thus, eachmetric reflects a likelihood that the transmitted signal actuallycorresponds to the associated possible symbol. The plurality of metricspreferably are distance-based metrics, and can be based on the distancesbetween a received signal (or processed received signal) and each signalpoint of the constellation set.

At step 1404, the plurality of metrics computed at step 1402 can besorted in an order based on the value of the metrics. The metrics can besorted in an ascending or descending order using a merge sortingalgorithm. Thus, as the merge sorting algorithm can sort groups ofmetrics at a time, the plurality of metrics preferably are sorted withina short amount of time. Then, at step 1406, a predetermined number ofmetrics can be selected from the sorted metrics, where the selectedmetrics preferably correspond to combinations of symbols that are likelyto have been transmitted during a symbol period. In particular,depending on whether the order is ascending or descending, apredetermined number of best metrics can be obtained by taking the firstK metrics or the last K metrics in the sorted sequence.

In some embodiments, the plurality of metrics can be calculated in aplurality of stages, where each stage is associated with a particularreceived signal. The metrics can be sorted by merge sorting only thosemetrics within a same stage, and a predetermined number of intermediatemetrics can be selected for each stage. Thus, the metrics for aparticular stage can be calculated based on the intermediate metricsfrom a previous stage. The intermediate metrics preferably areassociated with intermediate nodes in a tree diagram (e.g., tree diagram500 of FIG. 5) The predetermined number of selected metrics from a finalstage can be used to estimate the digital information. Thus, the finalselected metric preferably correspond to a predetermined number of leafsin the tree diagram.

It should be understood that flow diagram 1400 of FIG. 14 is merelyillustrative. Any of the steps in flow diagram 1400 may be modified,rearranged, or omitted, and any additional components may be added,without departing from the scope of the present invention.

Referring now to FIG. 15, flow diagram 1500 is shown for sorting aplurality of metrics computed by a detector in accordance with anembodiment of the present invention. In some embodiments, the steps offlow diagram 1500 can be incorporated into step 1404 of flow diagram1400 (FIG. 14). At step 1502, the plurality of metrics calculated foreach parent node may be sorted. The metrics may be sorted using a mergesort. There may be up to Q metrics to sort for a given parent node, asthere can be Q constellation points in a corresponding signalconstellation set. In other embodiments, there may be up to sqrt (Q)metrics, if each dimension of a complex constellation set is consideredseparately. In these embodiments, each parent node may have its metricssorted in two groups: one for the up to sqrt (Q) metrics of the realdimension, and one for the up to sqrt (Q) metrics of the imaginarydimension.

Thus, step 1502 can provide a plurality of groups of metrics, where eachgroup includes sorted metrics associated with a particular parent node.Then, at step 1504, each of these groups of metrics can be combined andsorted into a single group of metrics. The groups of metrics preferablyare sorted using a merge sort. In some embodiments, the groups of sortedmetrics can be combined and sorted one at a time. In other embodiments,multiple groups of sorted metrics can be sorted at once. Either way,when the metrics within a group are combined and sorted with othermetrics of other groups, the sorting may be done in parallel.

It should be understood that flow diagram 1500 of FIG. 15 is merelyillustrative. Any of the steps in flow diagram 1500 may be modified orrearranged, and any additional components may be added, withoutdeparting from the scope of the present invention.

Referring now to FIG. 16, illustrative flow diagram 1600 is shown forreducing the number of symbols considered by a detector in accordancewith an embodiment of the present invention. In some embodiments, thesteps of flow diagram 1600 can be incorporated into step 1308 of flowdiagram 1300 (FIG. 13), or can be used in place of steps 1402 and 1404in flow diagram 1400 (FIG. 14).

At step 1602, a region associated with a received signal is determinedfrom a set of predefined regions. The predefined regions may be similaror the same as those shown in FIGS. 11A and 11B for 16-QAM and 64-QAMsignal sets, respectively. A particular region may be selected based onthe value of a received signal. In fact, the value of a received signal,or the value of a processed version of the received signal, may fallwithin the boundaries of a region of the predefined regions, and thisregion may be selected as the associated region.

With continuing reference to FIG. 16, each predefined region preferablyis associated with a subset of symbols. In some embodiments, the subsetof symbols may also be predefined, and may be stored in read only memory(ROM). In these embodiments, the subset of symbols may be chosenaccording to a table lookup into the ROM, and addressing the memorybased on the region determined in step 1602. The subset of symbols foreach region may be based on the range of signal values within theboundaries of a region. For example, the subset of symbols for a regionmay include those with signal points within or around the boundaries ofthe region. Any suitable number of symbols may be selected in step 1604.In some embodiments, only the symbols that are extremely unlikely tocorrespond to the actual symbol are discarded. In other embodiments,only a few symbols that are either within the boundaries of the regionor very close to the boundaries are selected for the subset.

After a subset of symbols is selected from step 1604 of flow diagram1600, metrics preferably are calculated for each symbol in the subset.That is, rather than computing a metric for each signal point in asignal constellation set, metrics may be calculated for only thoseselected in step 1604. Once metrics are computed, the metrics may besorted at step 1608 using any of the techniques described above. Inparticular, the sorting may be performed using a merge sortingalgorithm.

It should be understood that flow diagram 1600 of FIG. 16 is merelyillustrative. Any of the steps in flow diagram 1600 may be modified,rearranged, or omitted, and any additional components may be added,without departing from the scope of the present invention.

Referring now to FIGS. 17A-17G, various exemplary implementations of thepresent invention are shown.

Referring now to FIG. 17A, the present invention can be implemented in ahard disk drive 1700. The present invention may implement either or bothsignal processing and/or control circuits, which are generallyidentified in FIG. 17A at 1702. In some implementations, the signalprocessing and/or control circuit 1702 and/or other circuits (not shown)in the HDD 1700 may process data, perform coding and/or encryption,perform calculations, and/or format data that is output to and/orreceived from a magnetic storage medium 1706.

The HDD 1700 may communicate with a host device (not shown) such as acomputer, mobile computing devices such as personal digital assistants,cellular phones, media or MP3 players and the like, and/or other devicesvia one or more wired or wireless communication links 1708. The HDD 1700may be connected to memory 1709 such as random access memory (RAM), lowlatency nonvolatile memory such as flash memory, read only memory (ROM)and/or other suitable electronic data storage.

Referring now to FIG. 17B, the present invention can be implemented in adigital versatile disc (DVD) drive 1710. The present invention mayimplement either or both signal processing and/or control circuits,which are generally identified in FIG. 17B at 1712, and/or mass datastorage of the DVD drive 1710. The signal processing and/or controlcircuit 1712 and/or other circuits (not shown) in the DVD 1710 mayprocess data, perform coding and/or encryption, perform calculations,and/or format data that is read from and/or data written to an opticalstorage medium 1716. In some implementations, the signal processingand/or control circuit 1712 and/or other circuits (not shown) in the DVD1710 can also perform other functions such as encoding and/or decodingand/or any other signal processing functions associated with a DVDdrive.

The DVD drive 1710 may communicate with an output device (not shown)such as a computer, television or other device via one or more wired orwireless communication links 1717. The DVD 1710 may communicate withmass data storage 1718 that stores data in a nonvolatile manner. Themass data storage 1718 may include a hard disk drive (HDD). The HDD mayhave the configuration shown in FIG. 17A. The HDD may be a mini HDD thatincludes one or more platters having a diameter that is smaller thanapproximately 1.8″. The DVD 1710 may be connected to memory 1719 such asRAM, ROM, low latency nonvolatile memory such as flash memory and/orother suitable electronic data storage.

Referring now to FIG. 17C, the present invention can be implemented in ahigh definition television (HDTV) 1720. The present invention mayimplement either or both signal processing and/or control circuits,which are generally identified in FIG. 17C at 1722, a WLAN interfaceand/or mass data storage of the HDTV 1720. The HDTV 1720 receives HDTVinput signals in either a wired or wireless format and generates HDTVoutput signals for a display 1726. In some implementations, signalprocessing circuit and/or control circuit 1722 and/or other circuits(not shown) of the HDTV 1720 may process data, perform coding and/orencryption, perform calculations, format data and/or perform any othertype of HDTV processing that may be required.

The HDTV 1720 may communicate with mass data storage 1727 that storesdata in a nonvolatile manner such as optical and/or magnetic storagedevices for example hard disk drives HDD and/or DVDs. At least one HDDmay have the configuration shown in FIG. 17A and/or at least one DVD mayhave the configuration shown in FIG. 17B. The HDD may be a mini HDD thatincludes one or more platters having a diameter that is smaller thanapproximately 1.8″. The HDTV 1720 may be connected to memory 1728 suchas RAM, ROM, low latency nonvolatile memory such as flash memory and/orother suitable electronic data storage. The HDTV 1720 also may supportconnections with a WLAN via a WLAN network interface 1729.

Referring now to FIG. 17D, the present invention implements a controlsystem of a vehicle 1730, a WLAN interface and/or mass data storage ofthe vehicle control system. In some implementations, the presentinvention may implement a powertrain control system 1732 that receivesinputs from one or more sensors such as temperature sensors, pressuresensors, rotational sensors, airflow sensors and/or any other suitablesensors and/or that generates one or more output control signals such asengine operating parameters, transmission operating parameters, and/orother control signals.

The present invention may also be implemented in other control systems1740 of the vehicle 1730. The control system 1740 may likewise receivesignals from input sensors 1742 and/or output control signals to one ormore output devices 1744. In some implementations, the control system1740 may be part of an anti-lock braking system (ABS), a navigationsystem, a telematics system, a vehicle telematics system, a lanedeparture system, an adaptive cruise control system, a vehicleentertainment system such as a stereo, DVD, compact disc and the like.Still other implementations are contemplated.

The powertrain control system 1732 may communicate with mass datastorage 1746 that stores data in a nonvolatile manner. The mass datastorage 1046 may include optical and/or magnetic storage devices forexample hard disk drives HDD and/or DVDs. At least one HDD may have theconfiguration shown in FIG. 17A and/or at least one DVD may have theconfiguration shown in FIG. 17B. The HDD may be a mini HDD that includesone or more platters having a diameter that is smaller thanapproximately 1.8″. The powertrain control system 1732 may be connectedto memory 1747 such as RAM, ROM, low latency nonvolatile memory such asflash memory and/or other suitable electronic data storage. Thepowertrain control system 1732 also may support connections with a WLANvia a WLAN network interface 1748. The control system 1740 may alsoinclude mass data storage, memory and/or a WLAN interface (all notshown).

Referring now to FIG. 17E, the present invention can be implemented in acellular phone 1750 that may include a cellular antenna 1751. Thepresent invention may implement either or both signal processing and/orcontrol circuits, which are generally identified in FIG. 17E at 1752, aWLAN interface and/or mass data storage of the cellular phone 1750. Insome implementations, the cellular phone 1750 includes a microphone1756, an audio output 1758 such as a speaker and/or audio output jack, adisplay 1760 and/or an input device 1762 such as a keypad, pointingdevice, voice actuation and/or other input device. The signal processingand/or control circuits 1752 and/or other circuits (not shown) in thecellular phone 1750 may process data, perform coding and/or encryption,perform calculations, format data and/or perform other cellular phonefunctions.

The cellular phone 1750 may communicate with mass data storage 1764 thatstores data in a nonvolatile manner such as optical and/or magneticstorage devices for example hard disk drives HDD and/or DVDs. At leastone HDD may have the configuration shown in FIG. 17A and/or at least oneDVD may have the configuration shown in FIG. 17B. The HDD may be a miniHDD that includes one or more platters having a diameter that is smallerthan approximately 1.8″. The cellular phone 1750 may be connected tomemory 1766 such as RAM, ROM, low latency nonvolatile memory such asflash memory and/or other suitable electronic data storage. The cellularphone 1750 also may support connections with a WLAN via a WLAN networkinterface 1768.

Referring now to FIG. 17F, the present invention can be implemented in aset top box 1780. The present invention may implement either or bothsignal processing and/or control circuits, which are generallyidentified in FIG. 17F at 1784, a WLAN interface and/or mass datastorage of the set top box 1780. The set top box 1780 receives signalsfrom a source such as a broadband source and outputs standard and/orhigh definition audio/video signals suitable for a display 1788 such asa television and/or monitor and/or other video and/or audio outputdevices. The signal processing and/or control circuits 1784 and/or othercircuits (not shown) of the set top box 1780 may process data, performcoding and/or encryption, perform calculations, format data and/orperform any other set top box function.

The set top box 1780 may communicate with mass data storage 1790 thatstores data in a nonvolatile manner. The mass data storage 1790 mayinclude optical and/or magnetic storage devices for example hard diskdrives HDD and/or DVDs. At least one HDD may have the configurationshown in FIG. 17A and/or at least one DVD may have the configurationshown in FIG. 17B. The HDD may be a mini HDD that includes one or moreplatters having a diameter that is smaller than approximately 1.8″. Theset top box 1780 may be connected to memory 1794 such as RAM, ROM, lowlatency nonvolatile memory such as flash memory and/or other suitableelectronic data storage. The set top box 1780 also may supportconnections with a WLAN via a WLAN network interface 1796.

Referring now to FIG. 17G, the present invention can be implemented in amedia player 1860. The present invention may implement either or bothsignal processing and/or control circuits, which are generallyidentified in FIG. 17G at 1804, a WLAN interface and/or mass datastorage of the media player 1800. In some implementations, the mediaplayer 1800 includes a display 1807 and/or a user input 1808 such as akeypad, touchpad and the like. In some implementations, the media player1800 may employ a graphical user interface (GUI) that typically employsmenus, drop down menus, icons and/or a point-and-click interface via thedisplay 1807 and/or user input 1808. The media player 1800 furtherincludes an audio output 1809 such as a speaker and/or audio outputjack. The signal processing and/or control circuits 1804 and/or othercircuits (not shown) of the media player 1800 may process data, performcoding and/or encryption, perform calculations, format data and/orperform any other media player function.

The media player 1800 may communicate with mass data storage 1810 thatstores data such as compressed audio and/or video content in anonvolatile manner. In some implementations, the compressed audio filesinclude files that are compliant with MP3 format or other suitablecompressed audio and/or video formats. The mass data storage may includeoptical and/or magnetic storage devices for example hard disk drives HDDand/or DVDs. At least one HDD may have the configuration shown in FIG.17A and/or at least one DVD may have the configuration shown in FIG.17B. The HDD may be a mini HDD that includes one or more platters havinga diameter that is smaller than approximately 1.8″. The media player1800 may be connected to memory 1814 such as RAM, ROM, low latencynonvolatile memory such as flash memory and/or other suitable electronicdata storage. The media player 1800 also may support connections with aWLAN via a WLAN network interface 1816. Still other implementations inaddition to those described above are contemplated.

The foregoing describes systems and methods for finding the nearestneighbors of a received signal for calculating soft information in amulti-level modulation scheme. Those skilled in the art will appreciatethat the invention can be practiced by other than the describedembodiments, which are presented for the purpose of illustration ratherthan of limitation.

What is claimed is:
 1. A method for reconstructing digital informationfrom a plurality of signals transmitted by a multiple-outputtransmitter, the method comprising: obtaining a plurality of signalsfrom the plurality of transmitted signals; calculating a plurality ofmetrics for each transmitted signal based on the plurality of obtainedsignals; sorting the metrics; and computing an estimate of the digitalinformation based on the sorted metrics.
 2. The method of claim 1,wherein the plurality of metrics are sorted using a merge sortingalgorithm.
 3. The method of claim 1, wherein each metric indicates alikelihood associated with a possible transmitted symbol.
 4. The methodof claim 1 further comprising selecting a subset of the sorted metrics,wherein computing an estimate of the digital information comprisescomputing an estimate of the digital information based on the subset ofsorted metrics.
 5. The method of claim 4, wherein the sorted metrics inthe subset of sorted metrics are associated with combinations of symbolsthat are likely to correspond to actually transmitted symbols.
 6. Themethod of claim 1, wherein calculating the plurality of metricscomprises calculating the metrics in a plurality of stages, and whereineach stage is associated with an obtained signal.
 7. The method of claim6, wherein sorting the metrics comprises sorting the metrics within asame stage, the method further comprising selecting an intermediate setof metrics for each stage.
 8. The method of claim 7, wherein calculatingmetrics comprises computing metrics based on an intermediate set ofmetrics from a previous stage.
 9. The method of claim 6, the methodfurther comprising selecting metrics from a final stage.
 10. The methodof claim 6, wherein each obtained signal is based on a complexmodulation scheme, and wherein each obtained signal is associated with:a first stage for computing metrics based on real components of thecomplex modulation scheme; and a second stage for computing metricsbased on imaginary components of the complex modulation scheme.
 11. Asystem for reconstructing digital information from a plurality ofsignals transmitted by a multiple-output transmitter, the systemcomprising: a receiver configured to receive a plurality of signalscorresponding to the plurality of transmitted signals; and a detector,comprising: a plurality of enumeration units configured to calculate aplurality of metrics for each transmitted signal based on processedsignals derived from the plurality of received signals; a plurality ofsorting units configured to sort the plurality of metrics; and acomputation unit configured to compute an estimate of the digitalinformation based on the sorted metrics.
 12. The system of claim 11,wherein the plurality of sorting units are further configured to sortthe plurality of metrics based on a merge sorting algorithm.
 13. Thesystem of claim 11, wherein each metric indicates a likelihoodassociated with a possible transmitted symbol.
 14. The system of claim11, wherein the plurality of sorting units are further configured toselect a subset of the sorted metrics, and wherein the computation unitis configured to compute an estimate of the digital information based onthe subset of sorted metrics.
 15. The system of claim 14, wherein thesorted metrics in the subset of sorted metrics are associated withcombinations of symbols that are most likely to correspond to actuallytransmitted symbols.
 16. The system of claim 11, wherein eachenumeration unit is further configured to calculate metrics associatedwith one of the processed signals.
 17. The system of claim 16, whereineach sorting unit is associated with an enumeration unit, and whereineach sorting unit is further configured to select an intermediate set ofmetrics.
 18. The system of claim 17, wherein each enumeration unit isfurther configured to calculate metrics based on an intermediate set ofmetrics received from one of the sorting units.
 19. The system of claim16, wherein a subset of the sorted metrics are selected from a finalsorting unit.
 20. The system of claim 16, wherein each processed signalis based on a complex modulation scheme, and wherein each processedsignal is associated with: a first one of the enumeration unitsconfigured to calculate metrics based on real components of the complexmodulation scheme; and a second one of the enumeration units configuredto calculate metrics based on imaginary components of the complexmodulation scheme.